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Learning Gated Bayesian Networks for Algorithmic Trading
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-8678-1164
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
2014 (English)In: Probabilistic Graphical Models: 7th European Workshop, PGM 2014, Utrecht, The Netherlands, September 17-19, 2014. Proceedings / [ed] Linda C. van der Gaag and Ad J. Feelders, Springer, 2014, p. 49-64Conference paper, Published paper (Refereed)
Abstract [en]

Gated Bayesian networks (GBNs) are a recently introduced extension of Bayesian networks that aims to model dynamical systems consisting of several distinct phases. In this paper, we present an algo- rithm for semi-automatic learning of GBNs. We use the algorithm to learn GBNs that output buy and sell decisions for use in algorithmic trading systems. We show how using the learnt GBNs can substantially lower risks towards invested capital, while at the same time generating similar or better rewards, compared to the benchmark investment strat- egy buy-and-hold. 

Place, publisher, year, edition, pages
Springer, 2014. p. 49-64
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 8754
Keywords [en]
Probabilistic graphical models, Bayesian networks, algorithmic trading, decision support
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-110777DOI: 10.1007/978-3-319-11433-0_4ISI: 000358253800004ISBN: 978-3-319-11432-3 (print)ISBN: 978-3-319-11433-0 (print)OAI: oai:DiVA.org:liu-110777DiVA, id: diva2:748876
Conference
7th European Workshop on Probabilistic Graphical Models, (PGM 2014), Utrecht, The Netherlands, September 17-19, 2014
Available from: 2014-09-22 Created: 2014-09-22 Last updated: 2018-02-19Bibliographically approved

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Bendtsen, MarcusPeña, Jose M.

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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  • sv-SE
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